Gene expression in cardiac surgery: impact of ischemia and reperfusion
Bibliographic record
Abstract
Ischemia and reperfusion (IR) invariably occurs during cardiac surgery. However, the transcriptional regulation of the myocardial and leukocyte genome in IR injury is incompletely elucidated. The primary hypothesis of this thesis is that IR injury occurring during cardiac surgery results in rapid induction of a specific pattern of gene transcription. The secondary hypothesis concerns the genomic and physiologic responses to protective brief periods of IR applied to the tissue located remotely from the heart (i.e., by a remote ischemic preconditioning (rIPC) stimulus). Gene expression was assessed primarily using microarray technology. In an initial study, we observed up-regulation of genes with possible cytoprotective functions in neonatal myocardium during ischemia. Global human myocardial gene expression during intra-operative IR injury was determined in a second study. A series of studies were then designed to examine the genomic and functional responses to a clinically-relevant rIPC stimulus. As leukocytes play a key role in IR injury, we identified the global genomic changes in human leukocytes following the rIPC stimulus. We then examined myocardial genomic responses in a mouse model, and, finally, demonstrated a major protective effect of rIPC in a porcine model of cardiopulmonary bypass (CPB) and intra-operative IR injury. Based on these data we applied the rIPC clinically with encouraging preliminary results. Overall the results of these studies indicate that intra-operative myocardial IR injury results in rapid induction of gene expression, and that this genomic response appears to be age-specific and can be modified by rIPC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".